Nat Commun. 2025 Apr 13;16(1):3506. doi: 10.1038/s41467-025-58883-3.

ABSTRACT

Deep learning (DL) enabled liquid-based cytology has potential for cervical cancer screening or triage. Here, we develop a DL model using whole cytology slides from 17,397 women and test it on 10,826 additional cases through a three-stage process. The DL model achieves robust performance across nine hospitals. In a multi-reader, multi-case study, it outperforms cytopathologists' sensitivity by 9%. Reading time significantly decreases with DL assistance (218s vs 30s; p < 0.0001). In community-based organized screening, the DL model's sensitivity matches that of senior cytopathologists (0.878 vs 0.854; p > 0.999), yet it has reduced specificity (0.831 vs 0.901; p < 0.0001). Notably, hospital-based opportunistic screening shows that junior cytopathologists with DL assistance significantly improve both their sensitivity and specificity (0.857 vs 0.657, 0.840 vs 0.737; both p < 0.0001). When triaging human papillomavirus-positive cases, DL assistance exhibits better performance than junior cytopathologists alone. These findings support using the DL model as an assistance tool in cervical screening and case triage.

PMID:40222978 | DOI:10.1038/s41467-025-58883-3

Sci Rep. 2025 Apr 13;15(1):12727. doi: 10.1038/s41598-025-94677-9.

ABSTRACT

In this paper, we propose a deep super-resolution generative adversarial network (DSR-GAN) combined with a convolutional neural network (CNN) model designed to classify four stages of Alzheimer's disease (AD): Mild Dementia (MD), Moderate Dementia (MOD), Non-Demented (ND), and Very Mild Dementia (VMD). The proposed DSR-GAN is implemented using a PyTorch library and uses a dataset of 6,400 MRI images. A super-resolution (SR) technique is applied to enhance the clarity and detail of the images, allowing the DSR-GAN to refine particular image features. The CNN model undergoes hyperparameter optimization and incorporates data augmentation strategies to maximize its efficiency. The normalized error matrix and area under ROC curve are used experimentally to evaluate the CNN's performance which achieved a testing accuracy of 99.22%, an area under the ROC curve of 100%, and an error rate of 0.0516. Also, the performance of the DSR-GAN is assessed using three different metrics: structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and multi-scale structural similarity index measure (MS-SSIM). The achieved SSIM score of 0.847, while the PSNR and MS-SSIM percentage are 29.30 dB and 96.39%, respectively. The combination of the DSR-GAN and CNN models provides a rapid and precise method to distinguish between various stages of Alzheimer's disease, potentially aiding professionals in the screening of AD cases.

PMID:40222973 | DOI:10.1038/s41598-025-94677-9

Neuroimage. 2025 Apr 11:121212. doi: 10.1016/j.neuroimage.2025.121212. Online ahead of print.

ABSTRACT

Short-range association fibers located in the superficial white matter play an important role in mediating higher-order cognitive function in humans. Detailed morphological characterization of short-range association fibers at the microscopic level promises to yield important insights into the axonal features driving cortico-cortical connectivity in the human brain yet has been difficult to achieve to date due to the challenges of imaging at nanometer-scale resolution over large tissue volumes. This work presents results from multi-beam scanning electron microscopy (EM) data acquired at 4 × 4 × 33 nm3 resolution in a volume of human superficial white matter measuring 200 × 200 × 112 μm (Braitenberg and Schüz, 2013), leveraging automated analysis methods. Myelin and myelinated axons were automatically segmented using deep convolutional neural networks (CNNs), assisted by transfer learning and dropout regularization techniques. A total of 128,285 myelinated axons were segmented, of which 70,321 and 2,102 were longer than 10 and 100 μm, respectively. Marked local variations in diameter (i.e., beading) and direction (i.e., undulation) were observed along the length of individual axons. Myelinated axons longer than 10 μm had inner diameters around 0.5 µm, outer diameters around 1 µm, and g-ratios around 0.5. This work fills a gap in knowledge of axonal morphometry in the superficial white matter and provides a large 3D human EM dataset and accurate segmentation results for a variety of future studies in different fields.

PMID:40222502 | DOI:10.1016/j.neuroimage.2025.121212

Neuroimage. 2025 Apr 11:121210. doi: 10.1016/j.neuroimage.2025.121210. Online ahead of print.

ABSTRACT

Alzheimer's disease (AD) represents a significant challenge due to its progressive neurodegenerative impact, particularly within an aging global demographic. This underscores the critical need for developing sophisticated diagnostic tools for its early detection and precise monitoring. Within this realm, PET/MR imaging stands out as a potent dual-modality approach that transforms sensor data into detailed perceptual mappings, thereby enriching our grasp of brain pathophysiology. To capitalize on the strengths of PET/MR imaging in diagnosing AD, we have introduced a novel deep learning framework named "IT", which is inspired by the Transformer architecture. This innovative model adeptly captures both local and global characteristics within the imaging data, refining these features through advanced feature engineering techniques to achieve a synergistic integration. The efficiency of our model is underscored by robust experimental validation, wherein it delivers superior performance on a host of evaluative benchmarks, all while maintaining low demands on computational resources. Furthermore, the features we extracted resonate with established medical theories regarding feature distribution and usage efficiency, enhancing the clinical relevance of our findings. These insights significantly bolster the arsenal of tools available for AD diagnostics and contribute to the broader narrative of deciphering brain functionality through state-of-the-art imaging modalities.

PMID:40222500 | DOI:10.1016/j.neuroimage.2025.121210

Comput Methods Programs Biomed. 2025 Apr 8;266:108754. doi: 10.1016/j.cmpb.2025.108754. Online ahead of print.

ABSTRACT

OBJECTIVE: Early fluid resuscitation is crucial in the treatment of sepsis, yet the optimal dosage remains debated. This study aims to determine the optimal multi-stage fluid resuscitation dosage for sepsis patients.

METHODS: We propose a reinforcement learning algorithm with neural networks (RL-NN), utilizing the flexibility of deep learning architectures to mitigate model misspecification. We use cross-validation and random search for hyperparameter tuning to further enhance model robustness and generalization.

RESULTS: Simulation results demonstrate that our method outperforms existing methods in terms of both the percentage of correctly classified optimal treatments and the predicted counterfactual mean outcome. Applying this method to the sepsis cohort from the Medical Information Mart for Intensive Care III (MIMIC-III), we recommend that all sepsis patients receive adequate fluid resuscitation (≥ 30 mL/kg) within the first 3 h of admission to the MICU. Our approach is expected to significantly reduce the mean SOFA score by 23.71%, enhancing patient outcomes.

CONCLUSION: Our RL-NN method offers an accurate, real-time approach to optimizing sepsis treatment and aligns with the 'Surviving Sepsis Campaign' guidelines. It also has the potential to be integrated with existing electronic health record (EHR) systems, guiding clinical decision-making and thereby improving patient prognosis.

PMID:40222267 | DOI:10.1016/j.cmpb.2025.108754

Eur Neuropsychopharmacol. 2025 Apr 12;95:40-48. doi: 10.1016/j.euroneuro.2025.03.013. Online ahead of print.

ABSTRACT

It is unclear if lithium and quetiapine have neuroprotective effects, especially in early stages of bipolar and schizoaffective disorders. Here, an age-related multivariate brain structural measure (i.e., brain-PAD) at baseline and changes in response to treatment after a first-episode mania (FEM) were examined. FEM participants were randomized to lithium (n=21) or quetiapine (n=18) monotherapy. T1-weighted scans were acquired at baseline, 3-months (FEM participants only) and 12-months. Brain age predictions for healthy controls (n=29) and young people with bipolar or schizoaffective disorder (15-25 years) were derived using a deep learning model trained on one of the largest datasets (N=53,542) to date. Notably, a higher brain-PAD value (predicted brain age - age) signifies an older-appearing brain. Baseline brain-PAD was higher in young people with FEM compared to controls (+1.70 year, p=0.04; Cohen's d=0.53 [SE=0.25], CI 95% [0.04 to 1.01]). However, no significant effects of time or treatment group, nor an interaction between the two, were observed throughout the course of the study. Baseline brain-PAD did not predict any change in symptomatic, quality of life or functional outcome scores over 12 months. In young individuals with FEM, baseline findings show their brains appeared older than controls. However, brain-PAD remained stable over time across treatment groups and neither baseline values nor treatment predicted 12-month outcomes. A longer follow-up with a larger sample is warranted to determine if treatment effects emerge later in bipolar and schizoaffective disorders. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry - ACTRN12607000639426.

PMID:40222151 | DOI:10.1016/j.euroneuro.2025.03.013

Ultramicroscopy. 2025 Apr 5;273:114137. doi: 10.1016/j.ultramic.2025.114137. Online ahead of print.

ABSTRACT

Precise alignment of the electron beam is critical for successful application of scanning transmission electron microscopes (STEM) to understanding materials at atomic level. Despite the success of aberration correctors, aberration correction is still a complex process. Here we approach aberration correction from the perspective of accelerator physics and show it is equivalent to minimizing the emittance growth of the beam, the span of the phase space distribution of the probe. We train a deep learning model to predict emittance growth from experimentally accessible Ronchigrams. Both simulation and experimental results show the model can capture the emittance variation with aberration coefficients accurately. We further demonstrate the model can act as a fast-executing function for the global optimization of the lens parameters. Our approach enables new ways to quickly quantify and automate aberration correction that takes advantage of the rapid measurements possible with high-speed electron cameras. In part II of the paper, we demonstrate how the emittance metric enables rapid online tuning of the aberration corrector using Bayesian optimization.

PMID:40222084 | DOI:10.1016/j.ultramic.2025.114137

Comput Biol Chem. 2025 Apr 5;118:108456. doi: 10.1016/j.compbiolchem.2025.108456. Online ahead of print.

ABSTRACT

The prevalent type of blood cancer is called leukemia, which is caused by the irregular production of immature malignant cells in the bone marrow. This dangerous condition weakens the immune system, making the body susceptible to infections, and can lead to death if not treated quickly. Thus, immediate treatments are necessary to detect leukemia at the initial stage to control abnormal cell growth. Leukemia detection from microscopic images of blood smears of malignant leukemia cells is a time-consuming and tedious task. Thus, a Tangent Sand Cat Swarm Optimization-Long Short-Term Memory-LeNet (TSCO-L-LeNet) with incremental learning is designed for the precise classification of acute lymphoblastic leukemia. The proposed model offers cheaper, faster and safer diagnosis service as the use of blood smear images reduces the diagnosis time and improves accuracy. Here, the input image is pre-processed using the adaptive median filter and the Scribble2label is used to segment the image. Later, the augmentation of segmented image is performed and the feature extraction process is employed to extract the necessary features from the augmented image. Finally, the L-LeNet with incremental learning is executed for acute lymphoblastic leukemia classification from the extracted features, where the TSCO approach is used to train the weights of L-LeNet. The experimental results show that TSCO-L-LeNet achieved maximum performance of 0.987 for accuracy, 0.977 for True Negative Rate (TNR), 0.967 for recall, 0.033 for False Negative rate, 0.023 for False Positive rate, and 0.979 for precision.

PMID:40222054 | DOI:10.1016/j.compbiolchem.2025.108456

Eur Radiol. 2025 Apr 13. doi: 10.1007/s00330-025-11574-5. Online ahead of print.

ABSTRACT

OBJECTIVE: This study aims to assess diagnostic performance of high-resolution dynamic contrast-enhanced (DCE) MRI with deep learning-based compressed sensing and super-resolution (DLCS-SR) reconstruction for identifying microadenomas.

MATERIALS AND METHODS: This prospective study included 126 participants with suspected pituitary microadenomas who underwent DCE MRI between June 2023 and January 2024. Four image groups were derived from single-scan DCE MRI, which included 1.5-mm slice thickness images using DLCS-SR (1.5-mm DLCS-SR images), 1.5-mm slice thickness images with deep learning-based compressed sensing reconstruction (1.5-mm DLCS images), 1.5-mm routine images, and 3-mm slice thickness images using DLCS-SR (3-mm DLCS-SR images). Diagnostic criteria were established by incorporating laboratory findings, clinical symptoms, medical histories, previous imaging, and certain pathologic reports. Two readers assessed the diagnostic performance in identifying pituitary abnormalities and microadenomas. Diagnostic agreements were assessed using κ statistics, and intergroup comparisons for microadenoma detection were performed using the DeLong and McNemar tests.

RESULTS: The 1.5-mm DLCS-SR images (κ = 0.746-0.848) exhibited superior diagnostic agreement, outperforming 1.5-mm DLCS (κ = 0.585-0.687), 1.5-mm routine (κ = 0.449-0.487), and 3-mm DLCS-SR images (κ = 0.347-0.369) (p < 0.001 for all). Additionally, the performance of 1.5-mm DLCS-SR images in identifying microadenomas [area under the receiver operating characteristic curve (AUC), 0.89-0.94] surpassed that of 1.5-mm DLCS (AUC, 0.83-0.87; p = 0.042 and 0.011, respectively), 1.5-mm routine (AUC, 0.76-0.78; p < 0.001), and 3-mm DLCS-SR images (AUC, 0.72-0.74; p < 0.001).

CONCLUSION: The findings revealed superior diagnostic performance of 1.5-mm DLCS-SR images in identifying pituitary abnormalities and microadenomas, indicating the clinical-potential of high-resolution DCE MRI.

KEY POINTS: Question What strategies can overcome the resolution limitations of conventional dynamic contrast-enhanced (DCE) MRI, and which contribute to a high false-negative rate in diagnosing pituitary microadenomas? Findings Deep learning-based compressed sensing and super-resolution reconstruction applied to DCE MRI achieved high resolution while improving image quality and diagnostic efficacy. Clinical relevance DCE MRI with a 1.5-mm slice thickness and high in-plane resolution, utilizing deep learning-based compressed sensing and super-resolution reconstruction, significantly enhances diagnostic accuracy for pituitary abnormalities and microadenomas, enabling timely and effective patient management.

PMID:40221940 | DOI:10.1007/s00330-025-11574-5

Dentomaxillofac Radiol. 2025 Apr 12:twaf027. doi: 10.1093/dmfr/twaf027. Online ahead of print.

ABSTRACT

OBJECTIVES: This systematic review aimed to assess the performance of artificial intelligence (AI) in the imaging diagnosis of maxillary sinusitis (MS) compared to human analysis.

METHODS: Studies that presented radiographic images for the diagnosis of paranasal sinus diseases, as well as control groups for AI, were included. Articles that performed tests on animals, presented other conditions, surgical methods, didn't present data on the diagnosis of MS or on the outcomes of interest (area under the curve, sensitivity, specificity, and accuracy), compared the outcome only among different AIs, were excluded. Searches were conducted in five electronic databases and a gray literature. The risk of bias (RB) was assessed using the QUADAS-2 and the certainty of evidence by GRADE.

RESULTS: Six studies were included. The type of study considered was retrospective observational; with serious RB, and a considerable heterogeneity in methodologies. The IA presents similar results to humans, however, imprecision was assessed as serious for the outcomes and the certainty of evidence was classified as very low according to the GRADE approach. Furthermore, a dose-response effect was determined, as specialists demonstrate greater mastery of the diagnosis of MS when compared to resident professionals or general clinicians.

CONCLUSIONS: Considering the outcomes, the AI represents a complementary tool for diagnosing MS, especially considering professionals with less experience. Finally, performance analysis and definition of comparison parameters should be encouraged considering future research perspectives.

ADVANCES IN KNOWLEDGE: AI can be used as a complementary tool for diagnosing MS, however studies are still lacking methodological standardization.

PMID:40221848 | DOI:10.1093/dmfr/twaf027

Nat Commun. 2025 Apr 12;16(1):3496. doi: 10.1038/s41467-025-58437-7.

ABSTRACT

Preeclampsia is a major cause of maternal and perinatal mortality with no known cure. Delivery timing is critical to balancing maternal and fetal risks. We develop and externally validate PEDeliveryTime, a class of clinically informative models which resulted from deep-learning models, to predict the time from PE diagnosis to delivery using electronic health records. We build the models on 1533 PE cases from the University of Michigan and validate it on 2172 preeclampsia cases from the University of Florida. PEDeliveryTime full model contains only 12 features yet achieves high c-index of 0.79 and 0.74 on the Michigan and Florida data set respectively. For the early-onset preeclampsia subset, the full model reaches 0.76 and 0.67 on the Michigan and Florida test sets. Collectively, these models perform an early assessment of delivery urgency and might help to better prioritize medical resources.

PMID:40221413 | DOI:10.1038/s41467-025-58437-7

Nat Commun. 2025 Apr 12;16(1):3491. doi: 10.1038/s41467-025-58481-3.

ABSTRACT

The three-dimensional organization of chromatin is essential for gene regulation and cellular function, with epigenome playing a key role. Hi-C methods have expanded our understanding of chromatin interactions, but their high cost and complexity limit their use. Existing models for predicting chromatin interactions rely on limited ChIP-seq inputs, reducing their accuracy and generalizability. In this work, we present a computational approach, EpiVerse, which leverages imputed epigenetic signals and advanced deep learning techniques. EpiVerse significantly improves the accuracy of cross-cell-type Hi-C prediction, while also enhancing model interpretability by incorporating chromatin state prediction within a multitask learning framework. Moreover, EpiVerse predicts Hi-C contact maps across an array of 39 human tissues, which provides a comprehensive view of the complex relationship between chromatin structure and gene regulation. Furthermore, EpiVerse facilitates unprecedented in silico perturbation experiments at the "epigenome-level" to unveil the chromatin architecture under specific conditions. EpiVerse is available on GitHub: https://github.com/jhhung/EpiVerse .

PMID:40221401 | DOI:10.1038/s41467-025-58481-3

Acad Radiol. 2025 Apr 11:S1076-6332(25)00291-0. doi: 10.1016/j.acra.2025.03.048. Online ahead of print.

ABSTRACT

RATIONALE AND OBJECTIVES: To construct a deep learning model (DL) based on high-resolution T2-weighted images for preoperative differentiation between T2 and T3 stage rectal cancer (RC), and to compare its performance with experienced radiologists.

METHODS: This retrospective study included 281 patients with pathologically confirmed RC from four centers (January 2017-December 2022). A DenseNet model was developed using 255 patients from three centers (training:validation ratio=8:2) and externally tested on 26 patients from a fourth center. Two experienced radiologists independently assessed T staging. Diagnostic performance was evaluated using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).

RESULTS: The DL model outperformed radiologists in differentiating T2 and T3 stages across all datasets. In the training set, the DL model achieved an AUC of 0.810, compared to 0.578 and 0.625 for radiologists A and B, respectively. In the external test set, the DL model maintained superior diagnostic performance (AUC=0.715) compared to radiologist A (AUC=0.549) and radiologist B (AUC=0.493). The DL model demonstrated higher accuracy for T2 staging (0.625-0.787) and T3 staging (0.611-0.814) compared to radiologists (0.373-0.526 for T2; 0.611-0.783 for T3), who showed a tendency to over-stage T2 tumors. Inter-observer agreement between radiologists was moderate (kappa=0.451).

CONCLUSION: The DenseNet-based DL model demonstrated superior accuracy and diagnostic efficiency than radiologists in preoperative differentiation between T2 and T3 stages RC. This automated approach could potentially improve staging accuracy and support clinical decision-making in RC treatment planning.

PMID:40221285 | DOI:10.1016/j.acra.2025.03.048

Radiol Clin North Am. 2025 May;63(3):477-490. doi: 10.1016/j.rcl.2024.11.005. Epub 2025 Jan 4.

ABSTRACT

While artificial intelligence (AI) has shown considerable progress in many areas of medical imaging, applications in abdominal imaging, particularly for the gastrointestinal (GI) system, have notably lagged behind advancements in other body regions. This article reviews foundational concepts in AI and highlights examples of AI applications in GI tract imaging. The discussion on AI applications includes acute & emergent GI imaging, inflammatory bowel disease, oncology, and other miscellaneous applications. It concludes with a discussion of important considerations for implementing AI tools in clinical practice, and steps we can take to accelerate future developments in the field.

PMID:40221188 | DOI:10.1016/j.rcl.2024.11.005

Neuroimage. 2025 Apr 10:121190. doi: 10.1016/j.neuroimage.2025.121190. Online ahead of print.

ABSTRACT

Diffusion MRI (dMRI) offers unique insights into the microstructure of fetal brain tissue in utero. Longitudinal and cross-sectional studies of fetal dMRI have the potential to reveal subtle but crucial changes associated with normal and abnormal neurodevelopment. However, these studies depend on precise spatial alignment of data across scans and subjects, which is particularly challenging in fetal imaging due to the low data quality, rapid brain development, and limited anatomical landmarks for accurate registration. Existing registration methods, primarily developed for superior-quality adult data, are not well-suited for addressing these complexities. To bridge this gap, we introduce FetDTIAlign, a deep learning approach tailored to fetal brain dMRI, enabling accurate affine and deformable registration. FetDTIAlign integrates a novel dual-encoder architecture and iterative feature-based inference, effectively minimizing the impact of noise and low resolution to achieve accurate alignment. Additionally, it strategically employs different network configurations and domain-specific image features at each registration stage, addressing the unique challenges of affine and deformable registration, enhancing both robustness and accuracy. We validated FetDTIAlign on a dataset covering gestational ages between 23 and 36 weeks, encompassing 60 white matter tracts. For all age groups, FetDTIAlign consistently showed superior anatomical correspondence and the best visual alignment in both affine and deformable registration, outperforming two classical optimization-based methods and a deep learning-based pipeline. Further validation on external data from the Developing Human Connectome Project demonstrated the generalizability of our method to data collected with different acquisition protocols. Our results show the feasibility of using deep learning for fetal brain dMRI registration, providing a more accurate and reliable alternative to classical techniques. By enabling precise cross-subject and tract-specific analyses, FetDTIAlign paves the way for new discoveries in early brain development. The code is available at https://gitlab.com/blibli/fetdtialign.

PMID:40221066 | DOI:10.1016/j.neuroimage.2025.121190

BMC Genomics. 2025 Apr 12;26(1):367. doi: 10.1186/s12864-025-11534-9.

ABSTRACT

BACKGROUND: The recent AI breakthrough of AlphaFold2 has revolutionized 3D protein structural modeling, proving crucial for protein design and variant effects prediction. However, intrinsically disordered regions-known for their lack of well-defined structure and lower sequence conservation-often yield low-confidence models. The latest Variant Effect Predictor (VEP), AlphaMissense, leverages AlphaFold2 models, achieving over 90% sensitivity and specificity in predicting variant effects. However, the effectiveness of tools for variants in disordered regions, which account for 30% of the human proteome, remains unclear.

RESULTS: In this study, we found that predicting pathogenicity for variants in disordered regions is less accurate than in ordered regions, particularly for mutations at the first N-Methionine site. Investigations into the efficacy of variant effect predictors on intrinsically disordered regions (IDRs) indicated that mutations in IDRs are predicted with lower sensitivity and the gap between sensitivity and specificity is largest in disordered regions, especially for AlphaMissense and VARITY.

CONCLUSIONS: The prevalence of IDRs within the human proteome, coupled with the increasing repertoire of biological functions they are known to perform, necessitated an investigation into the efficacy of state-of-the-art VEPs on such regions. This analysis revealed their consistently reduced sensitivity and differing prediction performance profile to ordered regions, indicating that new IDR-specific features and paradigms are needed to accurately classify disease mutations within those regions.

PMID:40221640 | DOI:10.1186/s12864-025-11534-9

Sci Rep. 2025 Apr 12;15(1):12552. doi: 10.1038/s41598-025-97074-4.

ABSTRACT

Landslides are among the geological disasters that frequently occur worldwide and significantly restrict the sustainable development of society. Therefore, it is of great practical significance to perform landslide susceptibility assessment. In addressing issues such as limited training samples, inadequate utilization of spatially effective features, and high computational costs associated with existing methods, we propose a landslide susceptibility assessment method (DS-DRN), which uses a lightweight dense residual network with emphasis on deep spatial features. To minimize computational costs, we design a depthwise separable residual module that optimizes traditional convolution on residual branches into depthwise separable convolution. Additionally, to prevent vanishing gradient and improve the reuse rate of landslide feature information, dense connections are employed to construct a deep feature extraction module. Finally, the output of the model is fed into the Softmax classifier for landslide susceptibility prediction. Taking Ya'an City in Sichuan Province as the study area, we compare the proposed DS-DRN method with three widely used deep learning methods: CNN, CPCNN-RF, and U-net. Evaluating model accuracy and performance, the DS-DRN method exhibits the highest prediction accuracy while also saving computational costs. Therefore, the proposed model can better fit the complex nonlinear relationship in landslide susceptibility, effectively mine deep spatial features, and address the high computational costs associated with complex networks.

PMID:40221608 | DOI:10.1038/s41598-025-97074-4

Sci Rep. 2025 Apr 12;15(1):12661. doi: 10.1038/s41598-025-97331-6.

ABSTRACT

Early screening for cancer has proven to improve the survival rate and spare patients from intensive and costly treatments due to late diagnosis. Cancer screening in the healthy population involves an initial risk stratification step to determine the screening method and frequency, primarily to optimize resource allocation by targeting screening towards individuals who draw most benefit. For most screening programs, age and clinical risk factors such as family history are part of the initial risk stratification algorithm. In this paper, we focus on developing a blood marker-based risk stratification approach, which could be used to identify patients with elevated cancer risk to be encouraged for taking a diagnostic test or participate in a screening program. We demonstrate that the combination of simple, widely available blood tests, such as complete blood count and complete metabolic panel, could potentially be used to identify patients at risk for colorectal, liver, and lung cancers with areas under the ROC curve of 0.76, 0.85, 0.78, respectively. Furthermore, we hypothesize that such an approach could not only be used as pre-screening risk assessment for individuals but also as population health management tool, for example to better interrogate the cancer risk in certain sub-populations.

PMID:40221571 | DOI:10.1038/s41598-025-97331-6

Sci Rep. 2025 Apr 12;15(1):12576. doi: 10.1038/s41598-025-97946-9.

ABSTRACT

The Green View Index (GVI) is utilized to evaluate urban street value and ecosystem services and to gauge public perceptions of street greening. This study investigates the spatial heterogeneity of the GVI and its influencing factors in Yuzhong District, Chongqing, a mountainous city in China. Deep learning algorithms were employed to calculate the green visibility of street view images, and Geographic Weighted Regression (GWR) and the Optimal Parameter-Based Geodetector (OPGD) were utilized to analyze the relationships between GVI and factors such as road physical attributes, the Normalized Difference Vegetation Index (NDVI), and topographic features. The results indicate that: (1) In Yuzhong District, 58.9% of streets have a GVI within a low to moderate range, suggesting room for improvement. Higher GVI levels are generally associated with elevated Digital Elevation Models (DEM), while slope, aspect, and terrain undulation have relatively minor overall impacts on GVI. (2) The GVI is highest in the western regions and lowest in the eastern regions, with streets along the riversides exhibiting lower GVI levels. (3) GWR analysis reveals that road type and NDVI significantly influence the GVI. Higher DEM values promote increased GVI, whereas high road density suppresses it. (4) The interaction between influencing factors drives the differentiated distribution of GVI within the study area. The interaction effects between Road type, NDVI, and DEM are particularly notable among these.

PMID:40221555 | DOI:10.1038/s41598-025-97946-9

Ann Surg Oncol. 2025 Apr 12. doi: 10.1245/s10434-025-17228-6. Online ahead of print.

ABSTRACT

BACKGROUND: Bladder cancer is one of the top ten cancers globally, with its incidence steadily rising in China. Early detection and prognosis risk assessment play a crucial role in guiding subsequent treatment decisions for bladder cancer. However, traditional diagnostic methods such as bladder endoscopy, imaging, or pathology examinations heavily rely on the clinical expertise and experience of clinicians, exhibiting subjectivity and poor reproducibility.

MATERIALS AND METHODS: With the rise of artificial intelligence, novel approaches, particularly those employing deep learning technology, have shown significant advancements in clinical tasks related to bladder cancer, including tumor detection, molecular subtyping identification, tumor staging and grading, prognosis prediction, and recurrence assessment.

RESULTS: Artificial intelligence, with its robust data mining capabilities, enhances diagnostic efficiency and reproducibility when assisting clinicians in decision-making, thereby reducing the risks of misdiagnosis and underdiagnosis. This not only helps alleviate the current challenges of talent shortages and uneven distribution of medical resources but also fosters the development of precision medicine.

CONCLUSIONS: This study provides a comprehensive review of the latest research advances and prospects of artificial intelligence technology in the precise diagnosis and treatment of bladder cancer.

PMID:40221553 | DOI:10.1245/s10434-025-17228-6